114,648 research outputs found
Shape Preserving Spline Interpolation
A rational spline solution to the problem of shape preserving interpolation is discussed. The rational spline is represented in terms of first derivative values at the knots and provides an alternative to the spline-under-tension. The idea of making the shape control parameters dependent on the first derivative unknowns is then explored. The monotonic or convex shape of the interpolation data can then be preserved automatically through the solution of the resulting non-linear consistency equations of the spline
The performance of approximating ordinary differential equations by neural nets
The dynamics of many systems are described by ordinary differential equations (ODE). Solving ODEs with standard methods (i.e. numerical integration) needs a high amount of computing time but only a small amount of storage memory. For some applications, e.g. short time weather forecast or real time robot control, long computation times are prohibitive. Is there a method which uses less computing time (but has drawbacks in other aspects, e.g. memory), so that the computation of ODEs gets faster? We will try to discuss this question for the assumption that the alternative computation method is a neural network which was trained on ODE dynamics and compare both methods using the same approximation error. This comparison is done with two different errors. First, we use the standard error that measures the difference between the approximation and the solution of the ODE which is hard to characterize. But in many cases, as for physics engines used in computer games, the shape of the approximation curve is important and not the exact values of the approximation. Therefore, we introduce a subjective error based on the Total Least Square Error (TLSE) which gives more consistent results. For the final performance comparison, we calculate the optimal resource usage for the neural network and evaluate it depending on the resolution of the interpolation points and the inter-point distance. Our conclusion gives a method to evaluate where neural nets are advantageous over numerical ODE integration and where this is not the case. Index Terms—ODE, neural nets, Euler method, approximation complexity, storage optimization
Better estimates from binned income data: Interpolated CDFs and mean-matching
Researchers often estimate income statistics from summaries that report the
number of incomes in bins such as \$0-10,000, \$10,001-20,000,...,\$200,000+.
Some analysts assign incomes to bin midpoints, but this treats income as
discrete. Other analysts fit a continuous parametric distribution, but the
distribution may not fit well.
We fit nonparametric continuous distributions that reproduce the bin counts
perfectly by interpolating the cumulative distribution function (CDF). We also
show how both midpoints and interpolated CDFs can be constrained to reproduce
the mean of income when it is known.
We compare the methods' accuracy in estimating the Gini coefficients of all
3,221 US counties. Fitting parametric distributions is very slow. Fitting
interpolated CDFs is much faster and slightly more accurate. Both interpolated
CDFs and midpoints give dramatically better estimates if constrained to match a
known mean.
We have implemented interpolated CDFs in the binsmooth package for R. We have
implemented the midpoint method in the rpme command for Stata. Both
implementations can be constrained to match a known mean.Comment: 20 pages (including Appendix), 3 tables, 2 figures (+2 in Appendix
Equation of state in 2+1 flavor QCD with improved Wilson quarks by the fixed scale approach
We study the equation of state in 2+1 flavor QCD with nonperturbatively
improved Wilson quarks coupled with the RG-improved Iwasaki glue. We apply the
-integration method to nonperturbatively calculate the equation of state by
the fixed-scale approach. With the fixed-scale approach, we can purely vary the
temperature on a line of constant physics without changing the system size and
renormalization constants. Unlike the conventional fixed- approach, it is
easy to keep scaling violations small at low temperature in the fixed scale
approach. We study 2+1 flavor QCD at light quark mass corresponding to
, while the strange quark mass is chosen around the
physical point. Although the light quark masses are heavier than the physical
values yet, our equation of state is roughly consistent with recent results
with highly improved staggered quarks at large .Comment: 14 pages, 12 figures, v2: Table I and Figure 3 are corrected,
reference updated. Main discussions and conclusions are unchanged, v3:
version to appear in PRD, v4: reference adde
B\'ezier curves that are close to elastica
We study the problem of identifying those cubic B\'ezier curves that are
close in the L2 norm to planar elastic curves. The problem arises in design
situations where the manufacturing process produces elastic curves; these are
difficult to work with in a digital environment. We seek a sub-class of special
B\'ezier curves as a proxy. We identify an easily computable quantity, which we
call the lambda-residual, that accurately predicts a small L2 distance. We then
identify geometric criteria on the control polygon that guarantee that a
B\'ezier curve has lambda-residual below 0.4, which effectively implies that
the curve is within 1 percent of its arc-length to an elastic curve in the L2
norm. Finally we give two projection algorithms that take an input B\'ezier
curve and adjust its length and shape, whilst keeping the end-points and
end-tangent angles fixed, until it is close to an elastic curve.Comment: 13 pages, 15 figure
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